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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

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        About MultiQC

        This report was generated using MultiQC, version 1.28

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the PavlidisLab/sc-annotation-pipeline analysis pipeline.
        Report generated on 2025-05-07, 17:44 PDT based on data in: /space/grp/rschwartz/rschwartz/cell_annotation_cortex.nf/work/1c/20b57ec5d0e9800083756280f8668b/PTSDBrainomics

        Sample Outlier Composition (outliers)

        Composition per sample of predicted outliers: outlier_mito = mitochondrial outliers; outlier_ribo = ribosomal outliers; outlier_hb = hemoglobin outliers; counts_outlier = outlier of log1p_n_genes ~ log1p_n_counts; predicted_doublet = doublets predicted by sc.pp.scrublet;.

        Created with MultiQC

        Sample cell type composition

        Composition per sample of predicted cell types.

        Created with MultiQC

        Leiden Cluster Composition (outliers)

        Leiden clustering performed on log-transformed data with top 2000 variable genes, 10 PCs, 30 neighbors, resolution = 0.3.

        Created with MultiQC

        Leiden cluster composition (cell types)

        Leiden clustering performed on log-transformed data with top 2000 variable genes, 10 PCs, 30 neighbors, resolution = 0.3.

        Created with MultiQC

        Cell type outlier composition

        Created with MultiQC

        Cell type markers

        Expression of select markers. Count are averaged across cell-type labels and z-score normalized across genes.

        Created with MultiQC

        Cell type UMAP

        UMAP of the cell type labels, sample names and leiden clusters.

        PTSDBrainomics | sc6 combined

        PTSDBrainomics | sc7 combined

        PTSDBrainomics | sc5 combined

        PTSDBrainomics | sc3 combined

        PTSDBrainomics | sc2 combined

        PTSDBrainomics | sc11 combined

        PTSDBrainomics | sc12 combined

        PTSDBrainomics | sc1 combined

        PTSDBrainomics | sc13 combined

        PTSDBrainomics | sc42 combined

        PTSDBrainomics | sc21 combined

        PTSDBrainomics | sc34 combined

        PTSDBrainomics | sc43 combined

        PTSDBrainomics | sc35 combined

        PTSDBrainomics | sc31 combined

        PTSDBrainomics | sc33 combined

        PTSDBrainomics | sc18 combined

        PTSDBrainomics | sc32 combined

        PTSDBrainomics | sc44 combined

        MultiQC v1.28 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap and FileSaver.js.